'''simple docstring'''




import unittest

from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import (
         MPNetForMaskedLM,
         MPNetForMultipleChoice,
         MPNetForQuestionAnswering,
         MPNetForSequenceClassification,
         MPNetForTokenClassification,
         MPNetModel,
     )

class 	_lowercase:



   """simple docstring"""





   def __init__(				self:     Dict      ,a:     Dict      ,a:     Dict=13      ,a:     Dict=7      ,a:     int=True      ,a:     Dict=True      ,a:     int=False      ,a:     Any=True      ,a:     List[Any]=99      ,a:     Union[str, Any]=64      ,a:     List[str]=5      ,a:     Tuple=4      ,a:     Tuple=64      ,a:     str="gelu"      ,a:     Tuple=0.1      ,a:     Optional[Any]=0.1      ,a:     Tuple=512      ,a:     Tuple=16      ,a:     List[Any]=2      ,a:     Optional[int]=0.02      ,a:     List[str]=3      ,a:     List[str]=4      ,a:     Optional[int]=None      ,):
    __UpperCAmelCase								=       parent
    __UpperCAmelCase								=       batch_size
    __UpperCAmelCase								=       seq_length
    __UpperCAmelCase								=       is_training
    __UpperCAmelCase								=       use_input_mask
    __UpperCAmelCase								=       use_token_type_ids
    __UpperCAmelCase								=       use_labels
    __UpperCAmelCase								=       vocab_size
    __UpperCAmelCase								=       hidden_size
    __UpperCAmelCase								=       num_hidden_layers
    __UpperCAmelCase								=       num_attention_heads
    __UpperCAmelCase								=       intermediate_size
    __UpperCAmelCase								=       hidden_act
    __UpperCAmelCase								=       hidden_dropout_prob
    __UpperCAmelCase								=       attention_probs_dropout_prob
    __UpperCAmelCase								=       max_position_embeddings
    __UpperCAmelCase								=       type_vocab_size
    __UpperCAmelCase								=       type_sequence_label_size
    __UpperCAmelCase								=       initializer_range
    __UpperCAmelCase								=       num_labels
    __UpperCAmelCase								=       num_choices
    __UpperCAmelCase								=       scope





   def       snake_case						(				self:     Optional[int]						):
    return MPNetConfig.from_pretrained('microsoft/mpnet-base'						)





   def       snake_case						(				self:     Tuple						):
    __UpperCAmelCase								=       ids_tensor([self.batch_size, self.seq_length]      ,self.vocab_size						)

    __UpperCAmelCase								=       None
    if self.use_input_mask:
     __UpperCAmelCase								=       random_attention_mask([self.batch_size, self.seq_length]						)

    __UpperCAmelCase								=       None
    __UpperCAmelCase								=       None
    __UpperCAmelCase								=       None
    if self.use_labels:
     __UpperCAmelCase								=       ids_tensor([self.batch_size]      ,self.type_sequence_label_size						)
     __UpperCAmelCase								=       ids_tensor([self.batch_size, self.seq_length]      ,self.num_labels						)
     __UpperCAmelCase								=       ids_tensor([self.batch_size]      ,self.num_choices						)

    __UpperCAmelCase								=       self.get_config()
    return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels





   def       snake_case						(				self:     Union[str, Any]						):
    return MPNetConfig(
        vocab_size=self.vocab_size      ,hidden_size=self.hidden_size      ,num_hidden_layers=self.num_hidden_layers      ,num_attention_heads=self.num_attention_heads      ,intermediate_size=self.intermediate_size      ,hidden_act=self.hidden_act      ,hidden_dropout_prob=self.hidden_dropout_prob      ,attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,max_position_embeddings=self.max_position_embeddings      ,initializer_range=self.initializer_range      ,)





   def       snake_case						(				self:     int      ,a:     List[Any]      ,a:     Any      ,a:     str      ,a:     List[str]      ,a:     List[str]      ,a:     List[Any]						):
    __UpperCAmelCase								=       MPNetModel(config=lowerCAmelCase__						)
    model.to(lowerCAmelCase__						)
    model.eval()
    __UpperCAmelCase								=       model(lowerCAmelCase__      ,lowerCAmelCase__						)
    __UpperCAmelCase								=       model(lowerCAmelCase__						)
    self.parent.assertEqual(result.last_hidden_state.shape      ,(self.batch_size, self.seq_length, self.hidden_size)						)
    self.parent.assertEqual(result.pooler_output.shape      ,(self.batch_size, self.hidden_size)						)





   def       snake_case						(				self:     List[Any]      ,a:     Any      ,a:     Optional[Any]      ,a:     List[Any]      ,a:     Optional[int]      ,a:     Optional[Any]      ,a:     Dict						):
    __UpperCAmelCase								=       MPNetForQuestionAnswering(config=lowerCAmelCase__						)
    model.to(lowerCAmelCase__						)
    model.eval()
    __UpperCAmelCase								=       model(
        lowerCAmelCase__      ,attention_mask=lowerCAmelCase__      ,start_positions=lowerCAmelCase__      ,end_positions=lowerCAmelCase__      ,)
    self.parent.assertEqual(result.start_logits.shape      ,(self.batch_size, self.seq_length)						)
    self.parent.assertEqual(result.end_logits.shape      ,(self.batch_size, self.seq_length)						)





   def       snake_case						(				self:     int      ,a:     Optional[int]      ,a:     Dict      ,a:     Any      ,a:     Optional[Any]      ,a:     Optional[int]      ,a:     int						):
    __UpperCAmelCase								=       self.num_labels
    __UpperCAmelCase								=       MPNetForSequenceClassification(lowerCAmelCase__						)
    model.to(lowerCAmelCase__						)
    model.eval()
    __UpperCAmelCase								=       model(lowerCAmelCase__      ,attention_mask=lowerCAmelCase__      ,labels=lowerCAmelCase__						)
    self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.num_labels)						)





   def       snake_case						(				self:     Dict      ,a:     Union[str, Any]      ,a:     int      ,a:     List[Any]      ,a:     Optional[Any]      ,a:     List[Any]      ,a:     Union[str, Any]						):
    __UpperCAmelCase								=       self.num_choices
    __UpperCAmelCase								=       MPNetForMultipleChoice(config=lowerCAmelCase__						)
    model.to(lowerCAmelCase__						)
    model.eval()
    __UpperCAmelCase								=       input_ids.unsqueeze(1						).expand(-1      ,self.num_choices      ,-1						).contiguous()
    __UpperCAmelCase								=       input_mask.unsqueeze(1						).expand(-1      ,self.num_choices      ,-1						).contiguous()
    __UpperCAmelCase								=       model(
        lowerCAmelCase__      ,attention_mask=lowerCAmelCase__      ,labels=lowerCAmelCase__      ,)
    self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.num_choices)						)





   def       snake_case						(				self:     List[str]      ,a:     Optional[int]      ,a:     List[Any]      ,a:     Dict      ,a:     Any      ,a:     str      ,a:     Any						):
    __UpperCAmelCase								=       self.num_labels
    __UpperCAmelCase								=       MPNetForTokenClassification(config=lowerCAmelCase__						)
    model.to(lowerCAmelCase__						)
    model.eval()
    __UpperCAmelCase								=       model(lowerCAmelCase__      ,attention_mask=lowerCAmelCase__      ,labels=lowerCAmelCase__						)
    self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.seq_length, self.num_labels)						)





   def       snake_case						(				self:     str						):
    __UpperCAmelCase								=       self.prepare_config_and_inputs()
    ((__UpperCAmelCase)	,      (__UpperCAmelCase)	,      (__UpperCAmelCase)	,      (__UpperCAmelCase)	,      (__UpperCAmelCase)	,      (__UpperCAmelCase))								=       config_and_inputs
    __UpperCAmelCase								=       {'input_ids': input_ids, 'attention_mask': input_mask}
    return config, inputs_dict




@require_torch
class 	_lowercase(  _UpperCAmelCase ,_UpperCAmelCase ,unittest.TestCase					):



   """simple docstring"""





   __lowerCamelCase     =	(
       (
           MPNetForMaskedLM,
           MPNetForMultipleChoice,
           MPNetForQuestionAnswering,
           MPNetForSequenceClassification,
           MPNetForTokenClassification,
           MPNetModel,
       )
       if is_torch_available()
       else ()
   )
   __lowerCamelCase     =	(
       {
           '''feature-extraction''': MPNetModel,
           '''fill-mask''': MPNetForMaskedLM,
           '''question-answering''': MPNetForQuestionAnswering,
           '''text-classification''': MPNetForSequenceClassification,
           '''token-classification''': MPNetForTokenClassification,
           '''zero-shot''': MPNetForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )
   __lowerCamelCase     =	False
   __lowerCamelCase     =	True





   def       snake_case						(				self:     Dict						):
    __UpperCAmelCase								=       MPNetModelTester(self						)
    __UpperCAmelCase								=       ConfigTester(self      ,config_class=lowerCAmelCase__      ,hidden_size=37						)





   def       snake_case						(				self:     Dict						):
    self.config_tester.run_common_tests()





   def       snake_case						(				self:     List[Any]						):
    __UpperCAmelCase								=       self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_mpnet_model(*lowerCAmelCase__						)





   def       snake_case						(				self:     List[Any]						):
    __UpperCAmelCase								=       self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_mpnet_for_sequence_classification(*lowerCAmelCase__						)





   def       snake_case						(				self:     str						):
    __UpperCAmelCase								=       self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_mpnet_for_multiple_choice(*lowerCAmelCase__						)





   def       snake_case						(				self:     List[Any]						):
    __UpperCAmelCase								=       self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_mpnet_for_token_classification(*lowerCAmelCase__						)





   def       snake_case						(				self:     List[str]						):
    __UpperCAmelCase								=       self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_mpnet_for_question_answering(*lowerCAmelCase__						)




@require_torch
class 	_lowercase(  unittest.TestCase					):



   """simple docstring"""





   @slow
   def       snake_case						(				self:     List[Any]						):
    __UpperCAmelCase								=       MPNetModel.from_pretrained('microsoft/mpnet-base'						)
    __UpperCAmelCase								=       torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]						)
    __UpperCAmelCase								=       model(lowerCAmelCase__						)[0]
    __UpperCAmelCase								=       torch.Size((1, 11, 768)						)
    self.assertEqual(output.shape      ,lowerCAmelCase__						)
    __UpperCAmelCase								=       torch.tensor(
        [[[-0.0550, 0.1943, -0.0740], [-0.0562, 0.2211, -0.0579], [-0.0437, 0.3337, -0.0641]]]						)
    # compare the actual values for a slice.
    self.assertTrue(torch.allclose(output[:, :3, :3]      ,lowerCAmelCase__      ,atol=1e-4						)						)




import argparse
from pathlib import Path

import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version

from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("""0.12.2"""):
		raise Exception("""requires fairseq >= 0.12.2""")
if version.parse(fairseq.__version__) > version.parse("""2"""):
		raise Exception("""requires fairseq < v2""")

logging.set_verbosity_info()
__a    :	Any         =    logging.get_logger(__name__)

__a    :	Union[str, Any]         =    """Hello, World!"""
__a    :	Optional[int]         =    """en_XX"""





def 							UpperCAmelCase		(       lowercase				,     lowercase				,     lowercase				):







				"""simple docstring"""



				__lowercase					     =				Path('''data_bin'''				)
				__lowercase					     =				FairseqXmodModel.from_pretrained(
				    model_name_or_path=str(Path(lowercase				).parent				)				,     checkpoint_file=Path(lowercase				).name				,     _name='''xmod_base'''				,     arch='''xmod_base'''				,     task='''multilingual_masked_lm'''				,     data_name_or_path=str(lowercase				)				,     bpe='''sentencepiece'''				,     sentencepiece_model=str(Path(lowercase				).parent / '''sentencepiece.bpe.model'''				)				,     src_dict=str(data_dir / '''dict.txt'''				)				,     )
				xmod.eval()  # disable dropout
				print(lowercase				)

				__lowercase					     =				xmod.model.encoder.sentence_encoder
				__lowercase					     =				XmodConfig(
				    vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings				,     hidden_size=xmod.cfg.model.encoder_embed_dim				,     num_hidden_layers=xmod.cfg.model.encoder_layers				,     num_attention_heads=xmod.cfg.model.encoder_attention_heads				,     intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim				,     max_position_embeddings=514				,     type_vocab_size=1				,     layer_norm_eps=1E-5				,     pre_norm=xmod.cfg.model.encoder_normalize_before				,     adapter_reduction_factor=getattr(xmod.cfg.model				,     '''bottleneck'''				,     2				)				,     adapter_layer_norm=xmod.cfg.model.adapter_layer_norm				,     adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm				,     ln_before_adapter=xmod.cfg.model.ln_before_adapter				,     languages=xmod.cfg.model.languages				,     )
				if classification_head:
								__lowercase					     =				xmod.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

				print('''Our X-MOD config:'''				,     lowercase				)

				__lowercase					     =				XmodForSequenceClassification(lowercase				) if classification_head else XmodForMaskedLM(lowercase				)
				model.eval()

				# Now let's copy all the weights.
				# Embeddings
				__lowercase					     =				xmod_sent_encoder.embed_tokens.weight
				__lowercase					     =				xmod_sent_encoder.embed_positions.weight
				__lowercase					     =				torch.zeros_like(
				    model.roberta.embeddings.token_type_embeddings.weight				)  # just zero them out b/c xmod doesn't use them.

				__lowercase					     =				xmod_sent_encoder.layernorm_embedding.weight
				__lowercase					     =				xmod_sent_encoder.layernorm_embedding.bias

				for i in range(config.num_hidden_layers				):
								# Encoder: start of layer
								__lowercase					     =				model.roberta.encoder.layer[i]
								__lowercase					     =				xmod_sent_encoder.layers[i]

								# self attention
								__lowercase					     =				layer.attention.self
								if not (
								    xmod_layer.self_attn.k_proj.weight.data.shape
								    == xmod_layer.self_attn.q_proj.weight.data.shape
								    == xmod_layer.self_attn.v_proj.weight.data.shape
								    == torch.Size((config.hidden_size, config.hidden_size)				)
								):
												raise AssertionError('''Dimensions of self-attention weights do not match.'''				)

								__lowercase					     =				xmod_layer.self_attn.q_proj.weight
								__lowercase					     =				xmod_layer.self_attn.q_proj.bias
								__lowercase					     =				xmod_layer.self_attn.k_proj.weight
								__lowercase					     =				xmod_layer.self_attn.k_proj.bias
								__lowercase					     =				xmod_layer.self_attn.v_proj.weight
								__lowercase					     =				xmod_layer.self_attn.v_proj.bias

								# self-attention output
								__lowercase					     =				layer.attention.output
								if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
												raise AssertionError('''Dimensions of self-attention output weights do not match.'''				)
								__lowercase					     =				xmod_layer.self_attn.out_proj.weight
								__lowercase					     =				xmod_layer.self_attn.out_proj.bias
								__lowercase					     =				xmod_layer.self_attn_layer_norm.weight
								__lowercase					     =				xmod_layer.self_attn_layer_norm.bias

								# intermediate
								__lowercase					     =				layer.intermediate
								if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
												raise AssertionError('''Dimensions of intermediate weights do not match.'''				)
								__lowercase					     =				xmod_layer.fca.weight
								__lowercase					     =				xmod_layer.fca.bias

								# output
								__lowercase					     =				layer.output
								if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
												raise AssertionError('''Dimensions of feed-forward weights do not match.'''				)
								__lowercase					     =				xmod_layer.fca.weight
								__lowercase					     =				xmod_layer.fca.bias
								__lowercase					     =				xmod_layer.final_layer_norm.weight
								__lowercase					     =				xmod_layer.final_layer_norm.bias
								if bert_output.adapter_layer_norm is not None:
												__lowercase					     =				xmod_layer.adapter_layer_norm.weight
												__lowercase					     =				xmod_layer.adapter_layer_norm.bias

								if sorted(bert_output.adapter_modules.keys()				) != sorted(xmod_layer.adapter_modules.keys()				):
												raise AssertionError('''Lists of language adapters do not match.'''				)
								for lang_code, adapter in xmod_layer.adapter_modules.items():
												__lowercase					     =				bert_output.adapter_modules[lang_code]
												__lowercase					     =				xmod_layer.adapter_modules[lang_code]
												__lowercase					     =				from_adapter.fca.weight
												__lowercase					     =				from_adapter.fca.bias
												__lowercase					     =				from_adapter.fca.weight
												__lowercase					     =				from_adapter.fca.bias

								# end of layer

				if xmod_sent_encoder.layer_norm is not None:
								__lowercase					     =				xmod_sent_encoder.layer_norm.weight
								__lowercase					     =				xmod_sent_encoder.layer_norm.bias

				if classification_head:
								__lowercase					     =				xmod.model.classification_heads['''mnli'''].dense.weight
								__lowercase					     =				xmod.model.classification_heads['''mnli'''].dense.bias
								__lowercase					     =				xmod.model.classification_heads['''mnli'''].out_proj.weight
								__lowercase					     =				xmod.model.classification_heads['''mnli'''].out_proj.bias
				else:
								# LM Head
								__lowercase					     =				xmod.model.encoder.lm_head.dense.weight
								__lowercase					     =				xmod.model.encoder.lm_head.dense.bias
								__lowercase					     =				xmod.model.encoder.lm_head.layer_norm.weight
								__lowercase					     =				xmod.model.encoder.lm_head.layer_norm.bias
								__lowercase					     =				xmod.model.encoder.lm_head.weight
								__lowercase					     =				xmod.model.encoder.lm_head.bias

				# Let's check that we get the same results.
				__lowercase					     =				xmod.encode(lowercase				).unsqueeze(0				)  # batch of size 1
				model.roberta.set_default_language(lowercase				)

				__lowercase					     =				model(lowercase				)[0]
				if classification_head:
								__lowercase					     =				xmod.model.classification_heads['''mnli'''](xmod.extract_features(lowercase				)				)
				else:
								__lowercase					     =				xmod.model(lowercase				,     lang_id=[SAMPLE_LANGUAGE]				)[0]
				print(our_output.shape				,     their_output.shape				)
				__lowercase					     =				torch.max(torch.abs(our_output - their_output				)				).item()
				print(F"max_absolute_diff = {max_absolute_diff}"				)  # ~ 1e-7
				__lowercase					     =				torch.allclose(lowercase				,     lowercase				,     atol=1E-3				)
				print('''Do both models output the same tensors?'''				,     '''🔥''' if success else '''💩'''				)
				if not success:
								raise Exception('''Something went wRoNg'''				)

				Path(lowercase				).mkdir(parents=lowercase				,     exist_ok=lowercase				)
				print(F"Saving model to {pytorch_dump_folder_path}"				)
				model.save_pretrained(lowercase				)


if __name__ == "__main__":
		__a    :	List[str]         =    argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--xmod_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
		)
		parser.add_argument(
		    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
		)
		parser.add_argument(
		    """--classification_head""", action="""store_true""", help="""Whether to convert a final classification head."""
		)
		__a    :	List[str]         =    parser.parse_args()
		convert_xmod_checkpoint_to_pytorch(
		    args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
		)